Theory of Constraints: "Blue Light" creating capacity for nothing (2007)
10 by strongpigeon | 1 comments on Hacker News.
10 by strongpigeon | 1 comments on Hacker News.
Friday, February 27, 2026
Thursday, February 26, 2026
New top story on Hacker News: Show HN: Mission Control – Open-source task management for AI agents
Show HN: Mission Control – Open-source task management for AI agents
11 by meisnerd | 1 comments on Hacker News.
I've been delegating work to Claude Code for the past few months, and it's been genuinely transformative—but managing multiple agents doing different things became chaos. No tool existed for this workflow, so I built one. The Problem When you're working with AI agents (Claude Code, Cursor, Windsurf), you end up in a weird situation: - You have tasks scattered across your head, Slack, email, and the CLI - Agents need clear work items, context, and role-specific instructions - You have no visibility into what agents are actually doing - Failed tasks just... disappear. No retry, no notification - Each agent context-switches constantly because you're hand-feeding them work I was manually shepherding agents, copying task descriptions, restarting failed sessions, and losing track of what needed done next. It felt like hiring expensive contractors but managing them like a disorganized chaos experiment. The Solution Mission Control is a task management app purpose-built for delegating work to AI agents. It's got the expected stuff (Eisenhower matrix, kanban board, goal hierarchy) but built from the assumption that your collaborators are Claude, not humans. The killer feature is the autonomous daemon . It runs in the background, polls your task queue, spawns Claude Code sessions automatically, handles retries, manages concurrency, and respects your cron-scheduled work. One click: your entire work queue activates. The Architecture - Local-first : Everything lives in JSON files. No database, no cloud dependency, no vendor lock-in. - Token-optimized API : The task/decision payloads are ~50 tokens vs ~5,400 unfiltered. Matters when you're spawning agents repeatedly. - Rock-solid concurrency : Zod validation + async-mutex locking prevents corruption under concurrent writes. - 193 automated tests : This thing has to be reliable. It's doing unattended work. The app is Next.js 15 with 5 built-in agent roles (researcher, developer, marketer, business-analyst, plus you). You define reusable skills as markdown that get injected into agent prompts. Agents report back through an inbox + decisions queue. Why Release This? A few people have asked for access, and I think it's genuinely useful for anyone delegating to AI. It's MIT licensed, open source, and actively maintained. What's Next - Human collaboration (sharing tasks with real team members) - Integrations with GitHub issues and email inboxes - Better observability dashboard for daemon execution - Custom agent templates (currently hardcoded roles) If you're doing something similar—delegating serious work to AI—check it out and let me know what's broken. GitHub: https://ift.tt/SEmlJNj
11 by meisnerd | 1 comments on Hacker News.
I've been delegating work to Claude Code for the past few months, and it's been genuinely transformative—but managing multiple agents doing different things became chaos. No tool existed for this workflow, so I built one. The Problem When you're working with AI agents (Claude Code, Cursor, Windsurf), you end up in a weird situation: - You have tasks scattered across your head, Slack, email, and the CLI - Agents need clear work items, context, and role-specific instructions - You have no visibility into what agents are actually doing - Failed tasks just... disappear. No retry, no notification - Each agent context-switches constantly because you're hand-feeding them work I was manually shepherding agents, copying task descriptions, restarting failed sessions, and losing track of what needed done next. It felt like hiring expensive contractors but managing them like a disorganized chaos experiment. The Solution Mission Control is a task management app purpose-built for delegating work to AI agents. It's got the expected stuff (Eisenhower matrix, kanban board, goal hierarchy) but built from the assumption that your collaborators are Claude, not humans. The killer feature is the autonomous daemon . It runs in the background, polls your task queue, spawns Claude Code sessions automatically, handles retries, manages concurrency, and respects your cron-scheduled work. One click: your entire work queue activates. The Architecture - Local-first : Everything lives in JSON files. No database, no cloud dependency, no vendor lock-in. - Token-optimized API : The task/decision payloads are ~50 tokens vs ~5,400 unfiltered. Matters when you're spawning agents repeatedly. - Rock-solid concurrency : Zod validation + async-mutex locking prevents corruption under concurrent writes. - 193 automated tests : This thing has to be reliable. It's doing unattended work. The app is Next.js 15 with 5 built-in agent roles (researcher, developer, marketer, business-analyst, plus you). You define reusable skills as markdown that get injected into agent prompts. Agents report back through an inbox + decisions queue. Why Release This? A few people have asked for access, and I think it's genuinely useful for anyone delegating to AI. It's MIT licensed, open source, and actively maintained. What's Next - Human collaboration (sharing tasks with real team members) - Integrations with GitHub issues and email inboxes - Better observability dashboard for daemon execution - Custom agent templates (currently hardcoded roles) If you're doing something similar—delegating serious work to AI—check it out and let me know what's broken. GitHub: https://ift.tt/SEmlJNj
Wednesday, February 25, 2026
Tuesday, February 24, 2026
Monday, February 23, 2026
New top story on Hacker News: Show HN: Shibuya – A High-Performance WAF in Rust with eBPF and ML Engine
Show HN: Shibuya – A High-Performance WAF in Rust with eBPF and ML Engine
14 by germainluperto | 7 comments on Hacker News.
Hi HN, I’ve been working on Shibuya, a next-generation Web Application Firewall (WAF) built from the ground up in Rust. I wanted to build a WAF that didn't just rely on legacy regex signatures but could understand intent and perform at line-rate using modern kernel features. What makes Shibuya different: Multi-Layer Pipeline: It integrates a high-performance proxy (built on Pingora) with rate limiting, bot detection, and threat intelligence. eBPF Kernel Filtering: For volumetric attacks, Shibuya can drop malicious packets at the kernel level using XDP before they consume userspace resources. Dual ML Engine: It uses an ONNX-based engine for anomaly detection and a Random Forest classifier to identify specific attack classes like SQLi, XSS, and RCE. API & GraphQL Protection: Includes deep inspection for GraphQL (depth and complexity analysis) and OpenAPI schema validation. WASM Extensibility: You can write and hot-load custom security logic using WebAssembly plugins. Ashigaru Lab: The project includes a deliberately vulnerable lab environment with 6 different services and a "Red Team Bot" to test the WAF against 100+ simulated payloads. The Dashboard: The dashboard is built with SvelteKit and offers real-time monitoring (ECharts), a "Panic Mode" for instant hardening, and a visual editor for the YAML configuration. I'm looking for feedback on the architecture and the performance of the Rust-eBPF integration.
14 by germainluperto | 7 comments on Hacker News.
Hi HN, I’ve been working on Shibuya, a next-generation Web Application Firewall (WAF) built from the ground up in Rust. I wanted to build a WAF that didn't just rely on legacy regex signatures but could understand intent and perform at line-rate using modern kernel features. What makes Shibuya different: Multi-Layer Pipeline: It integrates a high-performance proxy (built on Pingora) with rate limiting, bot detection, and threat intelligence. eBPF Kernel Filtering: For volumetric attacks, Shibuya can drop malicious packets at the kernel level using XDP before they consume userspace resources. Dual ML Engine: It uses an ONNX-based engine for anomaly detection and a Random Forest classifier to identify specific attack classes like SQLi, XSS, and RCE. API & GraphQL Protection: Includes deep inspection for GraphQL (depth and complexity analysis) and OpenAPI schema validation. WASM Extensibility: You can write and hot-load custom security logic using WebAssembly plugins. Ashigaru Lab: The project includes a deliberately vulnerable lab environment with 6 different services and a "Red Team Bot" to test the WAF against 100+ simulated payloads. The Dashboard: The dashboard is built with SvelteKit and offers real-time monitoring (ECharts), a "Panic Mode" for instant hardening, and a visual editor for the YAML configuration. I'm looking for feedback on the architecture and the performance of the Rust-eBPF integration.